What is A/B Test?
Running two versions of an ad simultaneously to measure which performs better. The version with higher CTR, conversion rate, or ROAS wins and gets scaled.
In mani: Mani generates multiple variations per brief specifically for A/B testing. Approve 2-3 variants and let the ad platform pick the winner.
How A/B testing works in paid ads
A/B testing (split testing) runs two ad variants simultaneously to identical audience segments. Each variant changes one variable: the headline, the image, the CTA, or the hook. The ad platform splits impressions evenly between variants and tracks which one drives more clicks, conversions, or revenue. Statistical significance typically requires 1,000+ impressions per variant. Most platforms flag a winner within 3-7 days at $20-50/day budgets.
What to test first
Test in order of impact: hook first (largest performance swing, often 2-5x), then image or video creative, then headline copy, then CTA text. Testing everything simultaneously muddies results. Change one element per test. AI tools accelerate this by generating 5+ variants of a single element while keeping everything else constant, letting you run multiple sequential tests in the time it used to take to run one.
Reading test results correctly
CTR tells you which creative gets attention. Conversion rate tells you which creative drives action. ROAS tells you which creative makes money. A high-CTR variant with low conversion rate means the hook is working but the landing page or offer is mismatched. Always optimize for the metric closest to revenue. On Meta, let Advantage+ auto-optimize after 3-5 days of even split testing.
Common A/B testing mistakes
Ending tests too early (before statistical significance). Testing too many variables at once (use multivariate tests for that). Ignoring audience segment differences (a winning hook for cold traffic may fail on retargeting). Not documenting results (you will forget what you tested 2 months ago). Keep a simple spreadsheet: date, variable tested, winner, margin of victory, sample size.